{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:37:21Z","timestamp":1772908641932,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,18]],"date-time":"2019-07-18T00:00:00Z","timestamp":1563408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the International Cooperation Project of the Ministry of Science and Technology of China","award":["No. 2010DFB83650"],"award-info":[{"award-number":["No. 2010DFB83650"]}]},{"DOI":"10.13039\/100007847","name":"Natural Science Foundation of Jilin Province","doi-asserted-by":"publisher","award":["No. 201501037JC"],"award-info":[{"award-number":["No. 201501037JC"]}],"id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles.<\/jats:p>","DOI":"10.3390\/s19143166","type":"journal-article","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T03:14:41Z","timestamp":1563506081000},"page":"3166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4915-5524","authenticated-orcid":false,"given":"Jingwei","family":"Cao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Chuanxue","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Shixin","family":"Song","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1847-504X","authenticated-orcid":false,"given":"Feng","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Silun","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bimbraw, K. (2015, January 21\u201323). Autonomous cars: Past, present and future: A review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology. Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Alsace, France.","DOI":"10.5220\/0005540501910198"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2584","DOI":"10.1109\/TITS.2017.2658662","article-title":"Overview of environment perception for intelligent vehicles","volume":"18","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MCOM.2019.1800226","article-title":"Dense moving fog for intelligent IoT: Key challenges and opportunities","volume":"57","author":"Andreev","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"ref_4","first-page":"1501","article-title":"A real-time driving assistance and surveillance system","volume":"25","author":"Chen","year":"2009","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Wang, G., Xu, G.Q., and Fu, G.Q. (2014, January 5\u201310). Safety driving assistance system design in intelligent vehicles. Proceedings of the 2014 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO), Bali, Indonesia.","DOI":"10.1109\/ROBIO.2014.7090740"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"D\u2019Cruz, C., and Ju, J.Z. (2007, January 16\u201319). Lane detection for driver assistance and intelligent vehicle applications. Proceedings of the 2007 International Symposium on Communications and Information Technologies (ISCIT), Sydney, Australia.","DOI":"10.1109\/ISCIT.2007.4392216"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kum, C.H., Cho, D.C., Ra, M.S., and Kim, W.Y. (2013, January 17\u201319). Lane detection system with around view monitoring for intelligent vehicle. Proceedings of the 2013 International SoC Design Conference (ISOCC), Busan, Korea.","DOI":"10.1109\/ISOCC.2013.6864011"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Scaramuzza, D., Censi, A., and Daniilidis, K. (2011, January 25\u201330). Exploiting motion priors in visual odometry for vehicle-mounted cameras with non-holonomic constraints. Proceedings of the 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics (IROS), San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6048856"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, B., Zhang, X.L., and Sato, M. (2014, January 19\u201323). Pitch angle estimation using a Vehicle-Mounted monocular camera for range measurement. Proceedings of the 2014 12th IEEE International Conference on Signal Processing (ICSP), Hangzhou, China.","DOI":"10.1109\/ICOSP.2014.7015183"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Schreiber, M., Konigshof, H., Hellmund, A., and Stiller, C. (2016, January 19\u201322). Vehicle localization with tightly coupled GNSS and visual odometry. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gotenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535488"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Y.L., Liang, W., He, H.S., and Tan, J.D. (2018, January 12\u201315). Perception of vehicle and traffic dynamics using visual-inertial sensors for assistive driving. Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ROBIO.2018.8665053"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, J.N., Ma, H.B., Zhang, X.H., and Liu, X.M. (2018, January 5\u20138). Detection of lane lines on both sides of road based on monocular camera. Proceedings of the 2018 IEEE International Conference on Mechatronics and Automation (ICMA), Changchun, China.","DOI":"10.1109\/ICMA.2018.8484630"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, Y.S., Zhang, W.B., Ji, X.W., Ren, C.X., and Wu, J. (2019). Research on lane a compensation method based on multi-sensor fusion. Sensors, 19.","DOI":"10.3390\/s19071584"},{"key":"ref_14","unstructured":"Zheng, B.G., Tian, B.X., Duan, J.M., and Gao, D.Z. (2008, January 1\u20133). Automatic detection technique of preceding lane and vehicle. Proceedings of the IEEE International Conference on Automation and Logistics (ICAL), Qingdao, China."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Haselhoff, A., and Kummert, A. (July, January 29). 2D line filters for vision-based lane detection and tracking. Proceedings of the 2009 International Workshop on Multidimensional (nD) Systems (nDS), Thessaloniki, Greece.","DOI":"10.1109\/NDS.2009.5196176"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.1016\/j.eswa.2014.10.024","article-title":"Real-time illumination invariant lane detection for lane departure warning system","volume":"42","author":"Son","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Amini, H., and Karasfi, B. (2016, January 9). New approach to road detection in challenging outdoor environment for autonomous vehicle. Proceedings of the 2016 Artificial Intelligence and Robotics (IRANOPEN), Qazvin, Iran.","DOI":"10.1109\/RIOS.2016.7529511"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TITS.2012.2216878","article-title":"Generalizing laplacian of gaussian filters for vanishing-point detection","volume":"14","author":"Kong","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hervieu, A., and Soheilian, B. (2013, January 23\u201326). Road side detection and reconstruction using LIDAR sensor. Proceedings of the 2013 IEEE Intelligent Vehicles Symposium (IEEE IV), Gold Coast, Australia.","DOI":"10.1109\/IVS.2013.6629637"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hata, A.Y., Osorio, F.S., and Wolf, D.F. (2014, January 8\u201311). Robust curb detection and vehicle localization in urban environments. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium (IV), Dearborn, MI, USA.","DOI":"10.1109\/IVS.2014.6856405"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/TPAMI.2013.185","article-title":"3D traffic scene understanding from movable platforms","volume":"36","author":"Geiger","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.advengsoft.2014.09.006","article-title":"Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm","volume":"79","author":"Liang","year":"2015","journal-title":"Adv. Eng. Softw."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bosaghzadeh, A., and Routeh, S.S. (2017, January 25\u201327). A novel PCA perspective mapping for robust lane detection in urban streets. Proceedings of the 19th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2017), Shiraz, Iran.","DOI":"10.1109\/AISP.2017.8324126"},{"key":"ref_24","unstructured":"He, B., Ai, R., Yan, Y., and Lang, X.P. (2016, January 19\u201322). Accurate and robust lane detection based on dual-view convolutional neutral network. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gotenburg, Sweden."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pan, X.G., Shi, J.P., Luo, P., Wang, X.G., and Tang, X.O. (2018, January 2\u20137). Spatial as deep: Spatial CNN for traffic scene understanding. Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"ref_27","unstructured":"Tan, H., Wang, J.F., Zhang, K., and Cui, S.M. (2012, January 18\u201320). Research on lane marking lines detection. Proceedings of the 2012 International Conference on Mechanical Engineering, Materials Science and Civil Engineering (ICMEMSCE), Harbin, China."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fernandez, C., Izquierdo, R., Llorca, D.F., and Sotelo, M.A. (2014, January 8\u201311). Road curb and lanes detection for autonomous driving on urban scenarios. Proceedings of the 2014 17th IEEE International Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.","DOI":"10.1109\/ITSC.2014.6957993"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.isprsjprs.2013.08.003","article-title":"An automated algorithm for extracting road edges from terrestrial mobile LiDAR data","volume":"85","author":"Kumar","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","unstructured":"Wang, Y.Q., Liang, B., and Zhuang, L.L. (2014, January 26\u201327). Applied technology in unstructured road detection with road environment based on SIFT-HARRIS. Proceedings of the 4th International Conference on Industry, Information System and Material Engineering (IISME), Nanjing, China."},{"key":"ref_31","unstructured":"Li, X.L., Ji, Y.F., Gao, Y., Feng, X.X., and Li, W.X. (2018, January 9\u201311). Unstructured road detection based on region growing. Proceedings of the 30th Chinese Control and Decision Conference (CCDC), Shenyang, China."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"24611","DOI":"10.1109\/ACCESS.2019.2898689","article-title":"A point cloud-based robust road curb detection and tracking method","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"C\u00e1cere Hern\u00e1ndez, D., Filonenko, A., Shahbaz, A., and Jo, K.H. (2017, January 17\u201319). Lane marking detection using image features and line fitting model. Proceedings of the 10th International Conference on Human System Interactions (HSI), Ulsan, Korea.","DOI":"10.1109\/HSI.2017.8005036"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, L., Luo, W.T., and Wang, K.C.P. (2018). Lane marking detection and reconstruction with line-scan imaging data. Sensors, 18.","DOI":"10.3390\/s18051635"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yang, W., Tang, X.L., and Liu, J. (2018). A fast learning method for accurate and robust lane detection using two-stage feature extraction with YOLO v3. Sensors, 18.","DOI":"10.3390\/s18124308"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.neunet.2016.12.002","article-title":"Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection","volume":"87","author":"Kim","year":"2017","journal-title":"Neural Netw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.neucom.2017.09.098","article-title":"Lane marking detection via deep convolutional neural network","volume":"280","author":"Tian","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Feng, J., Wu, Y., and Zhang, Y. (2018, January 8\u20139). Lane detection base on deep learning. Proceedings of the 2018 11th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID.2018.00078"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Brabandere, B.D., Gansbeke, W.V., Neven, D., Proesmans, M., and Gool, L.V. (2019). End-to-end lane detection through differentiable least-squares fitting. Comput. Sci.","DOI":"10.1109\/ICCVW.2019.00119"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1179\/1743131X14Y.0000000098","article-title":"Edge-detection method using binary morphology on hexagonal images","volume":"63","author":"Mostafa","year":"2015","journal-title":"Imaging Sci. J."},{"key":"ref_41","unstructured":"Singh, S., and Singh, R. (2015, January 11\u201313). Comparison of various edge detection techniques. Proceedings of the 2nd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yousaf, R.M., Habib, H.A., Dawood, H., and Shafiq, S. (2018, January 16\u201319). A comparative study of various edge detection methods. Proceedings of the 14th International Conference on Computational Intelligence and Security (CIS), Hangzhou, China.","DOI":"10.1109\/CIS2018.2018.00029"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1049\/el.2011.3577","article-title":"Concurrent visual navigation and localisation using inverse perspective transformation","volume":"48","author":"Burguera","year":"2012","journal-title":"Electron. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wu, Y.F., and Chen, Z.S. (2016, January 28\u201329). A detection method of road traffic sign based on inverse perspective transform. Proceedings of the 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), Chongqing, China.","DOI":"10.1109\/ICOACS.2016.7563100"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Waine, M., Rossa, C., Sloboda, R., Usmani, N., and Tavakoli, M. (2015, January 26\u201330). 3D shape visualization of curved needles in tissue from 2D ultrasound images using RANSAC. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139855"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cameron, K.J., and Bates, D.J. (2018, January 23\u201327). Geolocation with FDOA measurements via polynomial systems and RANSAC. Proceedings of the 2018 IEEE Radar Conference (RadarConf), Oklahoma City, OK, USA.","DOI":"10.1109\/RADAR.2018.8378640"},{"key":"ref_47","unstructured":"Davy, N., Bert, D.B., Stamatios, G., Marc, P., and Luc, V.G. (2018, January 26\u201330). Towards end-to-end lane detection: An instance segmentation approach. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, P.S., Yang, M., Wang, C.X., and Wang, B. (December, January 30). Multi-lane detection via multi-task network in various road scenes. Proceedings of the 2018 Chinese Automation Congress (CAC), Xi\u2019an, China.","DOI":"10.1109\/CAC.2018.8623762"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kuhnl, T., Kummert, F., and Fritsch, J. (2012, January 16\u201319). Spatial ray features for real-time ego-lane extraction. Proceedings of the 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), Anchorage, AK, USA.","DOI":"10.1109\/ITSC.2012.6338740"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1134\/S1054661818020049","article-title":"Improved lane line detection algorithm based on Hough transform","volume":"28","author":"Zheng","year":"2018","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Philion, J. (2019, January 16\u201321). FastDraw: Addressing the long tail of lane detection by adapting a sequential prediction network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01185"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zou, Q., Jiang, H.W., Dai, Q.Y., Yue, Y.H., Chen, L., and Wang, Q. (2019). Robust lane detection from continuous driving scenes using deep neural networks. Comput. Sci.","DOI":"10.1109\/TVT.2019.2949603"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3166\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:07:09Z","timestamp":1760188029000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3166"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,18]]},"references-count":52,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19143166"],"URL":"https:\/\/doi.org\/10.3390\/s19143166","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,18]]}}}